Overview

Dataset statistics

Number of variables17
Number of observations1484
Missing cells3459
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.2 KiB
Average record size in memory136.1 B

Variable types

Text13
Categorical3
Numeric1

Alerts

protein_category is highly imbalanced (72.1%)Imbalance
operating_regions is highly imbalanced (52.7%)Imbalance
technology_focus has 37 (2.5%) missing valuesMissing
product_type has 174 (11.7%) missing valuesMissing
animal_type_analog has 870 (58.6%) missing valuesMissing
ingredient_type has 466 (31.4%) missing valuesMissing
state has 948 (63.9%) missing valuesMissing
city has 158 (10.6%) missing valuesMissing
year_founded has 19 (1.3%) missing valuesMissing
founders has 634 (42.7%) missing valuesMissing
logo has 122 (8.2%) missing valuesMissing

Reproduction

Analysis started2024-01-14 19:01:42.379972
Analysis finished2024-01-14 19:01:43.964368
Duration1.58 second
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

company
Text

Distinct1483
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-01-14T11:01:44.079551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length68
Median length48
Mean length13.925876
Min length3

Characters and Unicode

Total characters20666
Distinct characters95
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1482 ?
Unique (%)99.9%

Sample

1st rowFrieslandCampina
2nd rowFinless Foods
3rd rowAvant Meats
4th rowUpside Foods
5th rowBioTech Foods
ValueCountFrequency (%)
foods 238
 
7.4%
food 58
 
1.8%
ltd 54
 
1.7%
the 51
 
1.6%
inc 38
 
1.2%
37
 
1.1%
meat 33
 
1.0%
co 32
 
1.0%
vegan 30
 
0.9%
plant 22
 
0.7%
Other values (1945) 2633
81.6%
2024-01-14T11:01:44.387493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1963
 
9.5%
o 1743
 
8.4%
e 1733
 
8.4%
a 1368
 
6.6%
i 1021
 
4.9%
n 966
 
4.7%
t 957
 
4.6%
r 936
 
4.5%
s 900
 
4.4%
d 709
 
3.4%
Other values (85) 8370
40.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14122
68.3%
Uppercase Letter 3926
 
19.0%
Space Separator 1965
 
9.5%
Other Punctuation 322
 
1.6%
Open Punctuation 87
 
0.4%
Close Punctuation 87
 
0.4%
Control 83
 
0.4%
Decimal Number 39
 
0.2%
Dash Punctuation 31
 
0.2%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1743
12.3%
e 1733
12.3%
a 1368
9.7%
i 1021
 
7.2%
n 966
 
6.8%
t 957
 
6.8%
r 936
 
6.6%
s 900
 
6.4%
d 709
 
5.0%
l 692
 
4.9%
Other values (30) 3097
21.9%
Uppercase Letter
ValueCountFrequency (%)
F 474
 
12.1%
M 268
 
6.8%
C 258
 
6.6%
P 249
 
6.3%
B 248
 
6.3%
S 244
 
6.2%
L 207
 
5.3%
A 206
 
5.2%
T 182
 
4.6%
V 175
 
4.5%
Other values (16) 1415
36.0%
Other Punctuation
ValueCountFrequency (%)
. 156
48.4%
' 65
20.2%
, 40
 
12.4%
& 22
 
6.8%
! 17
 
5.3%
/ 16
 
5.0%
: 3
 
0.9%
" 2
 
0.6%
* 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 8
20.5%
4 7
17.9%
2 5
12.8%
8 5
12.8%
5 4
10.3%
1 4
10.3%
3 3
 
7.7%
7 2
 
5.1%
6 1
 
2.6%
Space Separator
ValueCountFrequency (%)
1963
99.9%
  2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 86
98.9%
[ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 86
98.9%
] 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
| 1
50.0%
+ 1
50.0%
Control
ValueCountFrequency (%)
83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18048
87.3%
Common 2618
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1743
 
9.7%
e 1733
 
9.6%
a 1368
 
7.6%
i 1021
 
5.7%
n 966
 
5.4%
t 957
 
5.3%
r 936
 
5.2%
s 900
 
5.0%
d 709
 
3.9%
l 692
 
3.8%
Other values (56) 7023
38.9%
Common
ValueCountFrequency (%)
1963
75.0%
. 156
 
6.0%
( 86
 
3.3%
) 86
 
3.3%
83
 
3.2%
' 65
 
2.5%
, 40
 
1.5%
- 31
 
1.2%
& 22
 
0.8%
! 17
 
0.6%
Other values (19) 69
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20627
99.8%
None 37
 
0.2%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1963
 
9.5%
o 1743
 
8.5%
e 1733
 
8.4%
a 1368
 
6.6%
i 1021
 
4.9%
n 966
 
4.7%
t 957
 
4.6%
r 936
 
4.5%
s 900
 
4.4%
d 709
 
3.4%
Other values (69) 8331
40.4%
None
ValueCountFrequency (%)
é 13
35.1%
ä 4
 
10.8%
ú 3
 
8.1%
í 3
 
8.1%
  2
 
5.4%
ü 2
 
5.4%
ö 2
 
5.4%
ê 1
 
2.7%
ç 1
 
2.7%
ō 1
 
2.7%
Other values (5) 5
 
13.5%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

brief_description
Text

Distinct1469
Distinct (%)99.2%
Missing3
Missing (%)0.2%
Memory size11.7 KiB
2024-01-14T11:01:44.554351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length900
Median length255
Mean length97.080351
Min length12

Characters and Unicode

Total characters143776
Distinct characters96
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1459 ?
Unique (%)98.5%

Sample

1st rowDutch multinational dairy cooperative that has launched plant-based milks. In 2023, launched plant-based chicken brand Tender'lish.
2nd rowU.S.-based company working on plant-based fish and cultivated blue fin tuna
3rd rowHong Kong-based company using proprietary biotechnology platform to produce cultivated fish products, including food, skincare, and other functional applications
4th rowU.S.-based cultivated meat startup producing beef, duck, and chicken product prototypes
5th rowSpain-based startup producing cultivated meat products, acquired by Brazil-based JBS in 2021
ValueCountFrequency (%)
and 1019
 
5.2%
plant-based 837
 
4.3%
of 702
 
3.6%
company 625
 
3.2%
meat 428
 
2.2%
products 406
 
2.1%
that 376
 
1.9%
produces 372
 
1.9%
a 353
 
1.8%
the 308
 
1.6%
Other values (3009) 14252
72.4%
2024-01-14T11:01:44.842410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18660
13.0%
e 12666
 
8.8%
a 12397
 
8.6%
n 8597
 
6.0%
t 8172
 
5.7%
s 8048
 
5.6%
o 8042
 
5.6%
r 7231
 
5.0%
d 7018
 
4.9%
i 6829
 
4.7%
Other values (86) 46116
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116281
80.9%
Space Separator 18734
 
13.0%
Uppercase Letter 3228
 
2.2%
Other Punctuation 2864
 
2.0%
Dash Punctuation 2123
 
1.5%
Decimal Number 242
 
0.2%
Open Punctuation 92
 
0.1%
Close Punctuation 91
 
0.1%
Control 62
 
< 0.1%
Final Punctuation 36
 
< 0.1%
Other values (4) 23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12666
 
10.9%
a 12397
 
10.7%
n 8597
 
7.4%
t 8172
 
7.0%
s 8048
 
6.9%
o 8042
 
6.9%
r 7231
 
6.2%
d 7018
 
6.0%
i 6829
 
5.9%
c 5208
 
4.5%
Other values (20) 32073
27.6%
Uppercase Letter
ValueCountFrequency (%)
S 442
13.7%
P 363
11.2%
U 348
10.8%
B 191
 
5.9%
C 183
 
5.7%
M 180
 
5.6%
I 177
 
5.5%
F 175
 
5.4%
A 174
 
5.4%
K 142
 
4.4%
Other values (17) 853
26.4%
Other Punctuation
ValueCountFrequency (%)
. 1281
44.7%
, 1273
44.4%
" 126
 
4.4%
' 51
 
1.8%
/ 41
 
1.4%
& 37
 
1.3%
% 20
 
0.7%
; 15
 
0.5%
! 11
 
0.4%
: 6
 
0.2%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 79
32.6%
2 78
32.2%
1 29
 
12.0%
3 27
 
11.2%
4 6
 
2.5%
6 6
 
2.5%
5 5
 
2.1%
7 5
 
2.1%
9 4
 
1.7%
8 3
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 2115
99.6%
– 5
 
0.2%
— 3
 
0.1%
Space Separator
ValueCountFrequency (%)
18660
99.6%
  74
 
0.4%
Control
ValueCountFrequency (%)
49
79.0%
13
 
21.0%
Final Punctuation
ValueCountFrequency (%)
’ 31
86.1%
” 5
 
13.9%
Other Symbol
ValueCountFrequency (%)
™ 8
61.5%
® 5
38.5%
Initial Punctuation
ValueCountFrequency (%)
“ 6
85.7%
‘ 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 91
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119509
83.1%
Common 24267
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12666
 
10.6%
a 12397
 
10.4%
n 8597
 
7.2%
t 8172
 
6.8%
s 8048
 
6.7%
o 8042
 
6.7%
r 7231
 
6.1%
d 7018
 
5.9%
i 6829
 
5.7%
c 5208
 
4.4%
Other values (47) 35301
29.5%
Common
ValueCountFrequency (%)
18660
76.9%
- 2115
 
8.7%
. 1281
 
5.3%
, 1273
 
5.2%
" 126
 
0.5%
( 92
 
0.4%
) 91
 
0.4%
0 79
 
0.3%
2 78
 
0.3%
  74
 
0.3%
Other values (29) 398
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143632
99.9%
None 85
 
0.1%
Punctuation 51
 
< 0.1%
Letterlike Symbols 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18660
13.0%
e 12666
 
8.8%
a 12397
 
8.6%
n 8597
 
6.0%
t 8172
 
5.7%
s 8048
 
5.6%
o 8042
 
5.6%
r 7231
 
5.0%
d 7018
 
4.9%
i 6829
 
4.8%
Other values (72) 45972
32.0%
None
ValueCountFrequency (%)
  74
87.1%
® 5
 
5.9%
é 2
 
2.4%
ï 1
 
1.2%
â 1
 
1.2%
ñ 1
 
1.2%
Æ 1
 
1.2%
Punctuation
ValueCountFrequency (%)
’ 31
60.8%
“ 6
 
11.8%
– 5
 
9.8%
” 5
 
9.8%
— 3
 
5.9%
‘ 1
 
2.0%
Letterlike Symbols
ValueCountFrequency (%)
™ 8
100.0%

protein_category
Categorical

IMBALANCE 

Distinct41
Distinct (%)2.8%
Missing5
Missing (%)0.3%
Memory size11.7 KiB
Plant-based
1137 
Cultivated
145 
Biomass fermentation
 
52
Precision fermentation
 
51
Plant-based,Traditional fermentation
 
22
Other values (36)
 
72

Length

Max length136
Median length11
Mean length13.428668
Min length7

Characters and Unicode

Total characters19861
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.5%

Sample

1st rowPlant-based
2nd rowCultivated,Plant-based
3rd rowCultivated
4th rowCultivated
5th rowCultivated

Common Values

ValueCountFrequency (%)
Plant-based 1137
76.6%
Cultivated 145
 
9.8%
Biomass fermentation 52
 
3.5%
Precision fermentation 51
 
3.4%
Plant-based,Traditional fermentation 22
 
1.5%
Plant-based,Fermentation-derived 7
 
0.5%
Plant molecular farming 6
 
0.4%
Fermentation-derived 5
 
0.3%
Plant-based,Cultivated 5
 
0.3%
Cultivated,Fermentation-derived 4
 
0.3%
Other values (31) 45
 
3.0%
(Missing) 5
 
0.3%

Length

2024-01-14T11:01:44.974818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
plant-based 1137
67.4%
fermentation 151
 
9.0%
cultivated 145
 
8.6%
precision 58
 
3.4%
biomass 54
 
3.2%
plant-based,traditional 22
 
1.3%
fermentation,biomass 12
 
0.7%
molecular 11
 
0.7%
farming 10
 
0.6%
fermentation,precision 10
 
0.6%
Other values (28) 77
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a 2958
14.9%
t 2020
10.2%
e 1951
9.8%
n 1767
8.9%
d 1475
7.4%
l 1440
7.3%
s 1404
7.1%
P 1273
6.4%
- 1222
 
6.2%
b 1190
 
6.0%
Other values (15) 3161
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16724
84.2%
Uppercase Letter 1593
 
8.0%
Dash Punctuation 1222
 
6.2%
Space Separator 208
 
1.0%
Other Punctuation 114
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2958
17.7%
t 2020
12.1%
e 1951
11.7%
n 1767
10.6%
d 1475
8.8%
l 1440
8.6%
s 1404
8.4%
b 1190
7.1%
i 732
 
4.4%
o 415
 
2.5%
Other values (7) 1372
8.2%
Uppercase Letter
ValueCountFrequency (%)
P 1273
79.9%
C 170
 
10.7%
B 75
 
4.7%
T 43
 
2.7%
F 32
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 1222
100.0%
Space Separator
ValueCountFrequency (%)
208
100.0%
Other Punctuation
ValueCountFrequency (%)
, 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18317
92.2%
Common 1544
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2958
16.1%
t 2020
11.0%
e 1951
10.7%
n 1767
9.6%
d 1475
8.1%
l 1440
7.9%
s 1404
7.7%
P 1273
6.9%
b 1190
6.5%
i 732
 
4.0%
Other values (12) 2107
11.5%
Common
ValueCountFrequency (%)
- 1222
79.1%
208
 
13.5%
, 114
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2958
14.9%
t 2020
10.2%
e 1951
9.8%
n 1767
8.9%
d 1475
7.4%
l 1440
7.3%
s 1404
7.1%
P 1273
6.4%
- 1222
 
6.2%
b 1190
 
6.0%
Other values (15) 3161
15.9%

company_focus
Text

Distinct138
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2024-01-14T11:01:45.106647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length168
Median length163
Mean length11.450809
Min length4

Characters and Unicode

Total characters16993
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)5.7%

Sample

1st rowDairy
2nd rowMeat,Seafood
3rd rowMeat,Seafood
4th rowMeat
5th rowMeat
ValueCountFrequency (%)
dairy 474
23.3%
meat 459
22.5%
and 203
10.0%
meat,seafood 79
 
3.9%
infrastructure 70
 
3.4%
seafood 62
 
3.0%
eggs 49
 
2.4%
inputs 48
 
2.4%
ingredients 47
 
2.3%
meat,dairy 44
 
2.2%
Other values (116) 502
24.6%
2024-01-14T11:01:45.415372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2064
 
12.1%
e 1705
 
10.0%
t 1505
 
8.9%
r 1307
 
7.7%
i 1255
 
7.4%
n 996
 
5.9%
M 783
 
4.6%
o 707
 
4.2%
s 679
 
4.0%
D 648
 
3.8%
Other values (20) 5344
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13636
80.2%
Uppercase Letter 2121
 
12.5%
Other Punctuation 683
 
4.0%
Space Separator 553
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2064
15.1%
e 1705
12.5%
t 1505
11.0%
r 1307
9.6%
i 1255
9.2%
n 996
7.3%
o 707
 
5.2%
s 679
 
5.0%
y 648
 
4.8%
d 589
 
4.3%
Other values (8) 2181
16.0%
Uppercase Letter
ValueCountFrequency (%)
M 783
36.9%
D 648
30.6%
S 222
 
10.5%
I 133
 
6.3%
E 111
 
5.2%
O 108
 
5.1%
C 46
 
2.2%
B 39
 
1.8%
F 31
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 637
93.3%
/ 46
 
6.7%
Space Separator
ValueCountFrequency (%)
553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15757
92.7%
Common 1236
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2064
13.1%
e 1705
10.8%
t 1505
 
9.6%
r 1307
 
8.3%
i 1255
 
8.0%
n 996
 
6.3%
M 783
 
5.0%
o 707
 
4.5%
s 679
 
4.3%
D 648
 
4.1%
Other values (17) 4108
26.1%
Common
ValueCountFrequency (%)
, 637
51.5%
553
44.7%
/ 46
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2064
 
12.1%
e 1705
 
10.0%
t 1505
 
8.9%
r 1307
 
7.7%
i 1255
 
7.4%
n 996
 
5.9%
M 783
 
4.6%
o 707
 
4.2%
s 679
 
4.0%
D 648
 
3.8%
Other values (20) 5344
31.4%

company_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
Specialized (focused on alternative proteins)
1170 
Diversified
314 

Length

Max length45
Median length45
Mean length37.80593
Min length11

Characters and Unicode

Total characters56104
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiversified
2nd rowSpecialized (focused on alternative proteins)
3rd rowSpecialized (focused on alternative proteins)
4th rowSpecialized (focused on alternative proteins)
5th rowSpecialized (focused on alternative proteins)

Common Values

ValueCountFrequency (%)
Specialized (focused on alternative proteins) 1170
78.8%
Diversified 314
 
21.2%

Length

2024-01-14T11:01:45.548780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T11:01:45.672977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
specialized 1170
19.0%
focused 1170
19.0%
on 1170
19.0%
alternative 1170
19.0%
proteins 1170
19.0%
diversified 314
 
5.1%

Most occurring characters

ValueCountFrequency (%)
e 7648
13.6%
i 5622
 
10.0%
4680
 
8.3%
t 3510
 
6.3%
n 3510
 
6.3%
a 3510
 
6.3%
o 3510
 
6.3%
s 2654
 
4.7%
d 2654
 
4.7%
r 2654
 
4.7%
Other values (11) 16152
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47600
84.8%
Space Separator 4680
 
8.3%
Uppercase Letter 1484
 
2.6%
Close Punctuation 1170
 
2.1%
Open Punctuation 1170
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7648
16.1%
i 5622
11.8%
t 3510
 
7.4%
n 3510
 
7.4%
a 3510
 
7.4%
o 3510
 
7.4%
s 2654
 
5.6%
d 2654
 
5.6%
r 2654
 
5.6%
p 2340
 
4.9%
Other values (6) 9988
21.0%
Uppercase Letter
ValueCountFrequency (%)
S 1170
78.8%
D 314
 
21.2%
Space Separator
ValueCountFrequency (%)
4680
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49084
87.5%
Common 7020
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7648
15.6%
i 5622
11.5%
t 3510
 
7.2%
n 3510
 
7.2%
a 3510
 
7.2%
o 3510
 
7.2%
s 2654
 
5.4%
d 2654
 
5.4%
r 2654
 
5.4%
p 2340
 
4.8%
Other values (8) 11472
23.4%
Common
ValueCountFrequency (%)
4680
66.7%
) 1170
 
16.7%
( 1170
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7648
13.6%
i 5622
 
10.0%
4680
 
8.3%
t 3510
 
6.3%
n 3510
 
6.3%
a 3510
 
6.3%
o 3510
 
6.3%
s 2654
 
4.7%
d 2654
 
4.7%
r 2654
 
4.7%
Other values (11) 16152
28.8%

technology_focus
Text

MISSING 

Distinct123
Distinct (%)8.5%
Missing37
Missing (%)2.5%
Memory size11.7 KiB
2024-01-14T11:01:45.878451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length161
Median length41
Mean length44.173462
Min length10

Characters and Unicode

Total characters63919
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)5.9%

Sample

1st rowEnd product formulation and manufacturing
2nd rowCell culture media,End product formulation and manufacturing
3rd rowEnd product formulation and manufacturing
4th rowCell line development,End product formulation and manufacturing
5th rowEnd product formulation and manufacturing
ValueCountFrequency (%)
and 1363
18.5%
formulation 1308
17.8%
product 1308
17.8%
end 1242
16.9%
manufacturing 1209
16.4%
optimization 91
 
1.2%
culture 59
 
0.8%
ingredient 59
 
0.8%
line 58
 
0.8%
cell 50
 
0.7%
Other values (68) 609
8.3%
2024-01-14T11:01:46.227653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 7363
11.5%
5909
 
9.2%
a 5582
 
8.7%
u 5480
 
8.6%
t 4717
 
7.4%
o 4644
 
7.3%
d 4444
 
7.0%
r 4387
 
6.9%
i 3573
 
5.6%
m 2938
 
4.6%
Other values (19) 14882
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55871
87.4%
Space Separator 5909
 
9.2%
Uppercase Letter 1793
 
2.8%
Other Punctuation 346
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7363
13.2%
a 5582
10.0%
u 5480
9.8%
t 4717
8.4%
o 4644
8.3%
d 4444
8.0%
r 4387
7.9%
i 3573
 
6.4%
m 2938
 
5.3%
c 2933
 
5.2%
Other values (9) 9810
17.6%
Uppercase Letter
ValueCountFrequency (%)
E 1308
73.0%
I 142
 
7.9%
C 141
 
7.9%
B 89
 
5.0%
S 55
 
3.1%
T 20
 
1.1%
F 19
 
1.1%
H 19
 
1.1%
Space Separator
ValueCountFrequency (%)
5909
100.0%
Other Punctuation
ValueCountFrequency (%)
, 346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57664
90.2%
Common 6255
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7363
12.8%
a 5582
9.7%
u 5480
9.5%
t 4717
8.2%
o 4644
8.1%
d 4444
 
7.7%
r 4387
 
7.6%
i 3573
 
6.2%
m 2938
 
5.1%
c 2933
 
5.1%
Other values (17) 11603
20.1%
Common
ValueCountFrequency (%)
5909
94.5%
, 346
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7363
11.5%
5909
 
9.2%
a 5582
 
8.7%
u 5480
 
8.6%
t 4717
 
7.4%
o 4644
 
7.3%
d 4444
 
7.0%
r 4387
 
6.9%
i 3573
 
5.6%
m 2938
 
4.6%
Other values (19) 14882
23.3%

product_type
Text

MISSING 

Distinct170
Distinct (%)13.0%
Missing174
Missing (%)11.7%
Memory size11.7 KiB
2024-01-14T11:01:46.351019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length114
Median length87
Mean length20.251145
Min length4

Characters and Unicode

Total characters26529
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)8.2%

Sample

1st rowMilk
2nd rowWhole muscle meat/seafood
3rd rowGround meat/seafood
4th rowGround meat/seafood
5th rowGround meat/seafood,Whole muscle meat/seafood
ValueCountFrequency (%)
meat/seafood 549
20.0%
ground 342
12.5%
other 313
11.4%
muscle 207
 
7.5%
dairy 202
 
7.4%
meat/seafood,other 166
 
6.0%
whole 150
 
5.5%
milk 127
 
4.6%
cheese 125
 
4.6%
meat/seafood,ground 58
 
2.1%
Other values (96) 505
18.4%
2024-01-14T11:01:46.642184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3855
14.5%
o 2513
 
9.5%
a 2117
 
8.0%
d 1711
 
6.4%
t 1683
 
6.3%
s 1572
 
5.9%
1434
 
5.4%
r 1418
 
5.3%
h 1133
 
4.3%
m 1120
 
4.2%
Other values (19) 7973
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21494
81.0%
Uppercase Letter 1999
 
7.5%
Other Punctuation 1602
 
6.0%
Space Separator 1434
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3855
17.9%
o 2513
11.7%
a 2117
9.8%
d 1711
8.0%
t 1683
7.8%
s 1572
7.3%
r 1418
 
6.6%
h 1133
 
5.3%
m 1120
 
5.2%
f 954
 
4.4%
Other values (8) 3418
15.9%
Uppercase Letter
ValueCountFrequency (%)
O 679
34.0%
G 434
21.7%
C 265
 
13.3%
M 240
 
12.0%
W 207
 
10.4%
E 83
 
4.2%
I 68
 
3.4%
P 23
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 913
57.0%
, 689
43.0%
Space Separator
ValueCountFrequency (%)
1434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23493
88.6%
Common 3036
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3855
16.4%
o 2513
10.7%
a 2117
 
9.0%
d 1711
 
7.3%
t 1683
 
7.2%
s 1572
 
6.7%
r 1418
 
6.0%
h 1133
 
4.8%
m 1120
 
4.8%
f 954
 
4.1%
Other values (16) 5417
23.1%
Common
ValueCountFrequency (%)
1434
47.2%
/ 913
30.1%
, 689
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3855
14.5%
o 2513
 
9.5%
a 2117
 
8.0%
d 1711
 
6.4%
t 1683
 
6.3%
s 1572
 
5.9%
1434
 
5.4%
r 1418
 
5.3%
h 1133
 
4.3%
m 1120
 
4.2%
Other values (19) 7973
30.1%

animal_type_analog
Text

MISSING 

Distinct152
Distinct (%)24.8%
Missing870
Missing (%)58.6%
Memory size11.7 KiB
2024-01-14T11:01:46.763311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length77
Median length54
Mean length15.434853
Min length4

Characters and Unicode

Total characters9477
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)16.6%

Sample

1st rowFish
2nd rowFish
3rd rowDuck,Beef/veal,Chicken
4th rowBeef/veal
5th rowBeef/veal
ValueCountFrequency (%)
beef/veal 105
17.1%
fish 49
 
8.0%
chicken 38
 
6.2%
beef/veal,chicken 32
 
5.2%
other 31
 
5.0%
beef/veal,pork 25
 
4.1%
pork 25
 
4.1%
chicken,beef/veal 22
 
3.6%
pork,beef/veal 14
 
2.3%
beef/veal,chicken,pork 13
 
2.1%
Other values (142) 260
42.3%
2024-01-14T11:01:47.018589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1577
16.6%
, 730
 
7.7%
h 598
 
6.3%
k 572
 
6.0%
l 513
 
5.4%
a 495
 
5.2%
i 495
 
5.2%
f 430
 
4.5%
/ 421
 
4.4%
B 386
 
4.1%
Other values (19) 3260
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6982
73.7%
Uppercase Letter 1344
 
14.2%
Other Punctuation 1151
 
12.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1577
22.6%
h 598
 
8.6%
k 572
 
8.2%
l 513
 
7.3%
a 495
 
7.1%
i 495
 
7.1%
f 430
 
6.2%
v 386
 
5.5%
n 369
 
5.3%
r 343
 
4.9%
Other values (8) 1204
17.2%
Uppercase Letter
ValueCountFrequency (%)
B 386
28.7%
C 294
21.9%
P 228
17.0%
F 174
12.9%
T 92
 
6.8%
O 59
 
4.4%
S 48
 
3.6%
M 35
 
2.6%
D 28
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 730
63.4%
/ 421
36.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 8326
87.9%
Common 1151
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1577
18.9%
h 598
 
7.2%
k 572
 
6.9%
l 513
 
6.2%
a 495
 
5.9%
i 495
 
5.9%
f 430
 
5.2%
B 386
 
4.6%
v 386
 
4.6%
n 369
 
4.4%
Other values (17) 2505
30.1%
Common
ValueCountFrequency (%)
, 730
63.4%
/ 421
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1577
16.6%
, 730
 
7.7%
h 598
 
6.3%
k 572
 
6.0%
l 513
 
5.4%
a 495
 
5.2%
i 495
 
5.2%
f 430
 
4.5%
/ 421
 
4.4%
B 386
 
4.1%
Other values (19) 3260
34.4%

ingredient_type
Text

MISSING 

Distinct422
Distinct (%)41.5%
Missing466
Missing (%)31.4%
Memory size11.7 KiB
2024-01-14T11:01:47.141453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length160
Median length70
Mean length12.017682
Min length3

Characters and Unicode

Total characters12234
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique344 ?
Unique (%)33.8%

Sample

1st rowOat,Soy
2nd rowSoy
3rd rowSoy,Almond,Rice,Oat
4th rowSoy,Pea
5th rowMung Bean
ValueCountFrequency (%)
soy 133
 
12.4%
cashew 53
 
4.9%
pea 39
 
3.6%
coconut 37
 
3.4%
oat 34
 
3.2%
other 31
 
2.9%
soy,wheat 27
 
2.5%
almond 26
 
2.4%
mushrooms 22
 
2.0%
algae 15
 
1.4%
Other values (438) 658
61.2%
2024-01-14T11:01:47.714401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1222
 
10.0%
a 1196
 
9.8%
e 1124
 
9.2%
, 1088
 
8.9%
t 812
 
6.6%
n 560
 
4.6%
h 504
 
4.1%
c 412
 
3.4%
S 409
 
3.3%
C 409
 
3.3%
Other values (37) 4498
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8951
73.2%
Uppercase Letter 2131
 
17.4%
Other Punctuation 1088
 
8.9%
Space Separator 64
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1222
13.7%
a 1196
13.4%
e 1124
12.6%
t 812
9.1%
n 560
 
6.3%
h 504
 
5.6%
c 412
 
4.6%
u 400
 
4.5%
y 388
 
4.3%
s 385
 
4.3%
Other values (15) 1948
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 409
19.2%
C 409
19.2%
P 295
13.8%
O 189
8.9%
A 160
 
7.5%
W 156
 
7.3%
M 103
 
4.8%
B 85
 
4.0%
R 85
 
4.0%
Y 42
 
2.0%
Other values (10) 198
9.3%
Other Punctuation
ValueCountFrequency (%)
, 1088
100.0%
Space Separator
ValueCountFrequency (%)
64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11082
90.6%
Common 1152
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1222
 
11.0%
a 1196
 
10.8%
e 1124
 
10.1%
t 812
 
7.3%
n 560
 
5.1%
h 504
 
4.5%
c 412
 
3.7%
S 409
 
3.7%
C 409
 
3.7%
u 400
 
3.6%
Other values (35) 4034
36.4%
Common
ValueCountFrequency (%)
, 1088
94.4%
64
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1222
 
10.0%
a 1196
 
9.8%
e 1124
 
9.2%
, 1088
 
8.9%
t 812
 
6.6%
n 560
 
4.6%
h 504
 
4.1%
c 412
 
3.4%
S 409
 
3.3%
C 409
 
3.3%
Other values (37) 4498
36.8%

operating_regions
Categorical

IMBALANCE 

Distinct40
Distinct (%)2.7%
Missing10
Missing (%)0.7%
Memory size11.7 KiB
Europe
499 
U.S. and Canada
448 
Asia Pacific
262 
Latin America
98 
Africa/Middle East
61 
Other values (35)
106 

Length

Max length90
Median length68
Mean length12.5
Min length6

Characters and Unicode

Total characters18425
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)1.4%

Sample

1st rowEurope
2nd rowU.S. and Canada
3rd rowAsia Pacific
4th rowU.S. and Canada
5th rowEurope

Common Values

ValueCountFrequency (%)
Europe 499
33.6%
U.S. and Canada 448
30.2%
Asia Pacific 262
17.7%
Latin America 98
 
6.6%
Africa/Middle East 61
 
4.1%
Australia/New Zealand 27
 
1.8%
U.S. and Canada,Europe 12
 
0.8%
U.S. and Canada,Asia Pacific 9
 
0.6%
Europe,U.S. and Canada 6
 
0.4%
Asia Pacific,U.S. and Canada 6
 
0.4%
Other values (30) 46
 
3.1%
(Missing) 10
 
0.7%

Length

2024-01-14T11:01:47.848870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 513
16.9%
europe 499
16.5%
u.s 489
16.1%
canada 467
15.4%
pacific 281
9.3%
asia 271
8.9%
latin 107
 
3.5%
america 104
 
3.4%
east 70
 
2.3%
africa/middle 63
 
2.1%
Other values (33) 169
 
5.6%

Most occurring characters

ValueCountFrequency (%)
a 3187
17.3%
1559
 
8.5%
i 1334
 
7.2%
d 1217
 
6.6%
n 1179
 
6.4%
. 1026
 
5.6%
e 815
 
4.4%
c 801
 
4.3%
r 782
 
4.2%
E 629
 
3.4%
Other values (22) 5896
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12333
66.9%
Uppercase Letter 3270
 
17.7%
Space Separator 1559
 
8.5%
Other Punctuation 1263
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3187
25.8%
i 1334
10.8%
d 1217
 
9.9%
n 1179
 
9.6%
e 815
 
6.6%
c 801
 
6.5%
r 782
 
6.3%
u 583
 
4.7%
o 553
 
4.5%
p 550
 
4.5%
Other values (7) 1332
10.8%
Uppercase Letter
ValueCountFrequency (%)
E 629
19.2%
A 533
16.3%
S 513
15.7%
U 513
15.7%
C 513
15.7%
P 301
9.2%
L 120
 
3.7%
M 79
 
2.4%
N 33
 
1.0%
Z 33
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 1026
81.2%
, 125
 
9.9%
/ 112
 
8.9%
Space Separator
ValueCountFrequency (%)
1559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15603
84.7%
Common 2822
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3187
20.4%
i 1334
 
8.5%
d 1217
 
7.8%
n 1179
 
7.6%
e 815
 
5.2%
c 801
 
5.1%
r 782
 
5.0%
E 629
 
4.0%
u 583
 
3.7%
o 553
 
3.5%
Other values (18) 4523
29.0%
Common
ValueCountFrequency (%)
1559
55.2%
. 1026
36.4%
, 125
 
4.4%
/ 112
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3187
17.3%
1559
 
8.5%
i 1334
 
7.2%
d 1217
 
6.6%
n 1179
 
6.4%
. 1026
 
5.6%
e 815
 
4.4%
c 801
 
4.3%
r 782
 
4.2%
E 629
 
3.4%
Other values (22) 5896
32.0%

country_region
Text

Distinct62
Distinct (%)4.2%
Missing5
Missing (%)0.3%
Memory size11.7 KiB
2024-01-14T11:01:47.995699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length9.5003381
Min length4

Characters and Unicode

Total characters14051
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.5%

Sample

1st rowNetherlands
2nd rowUnited States
3rd rowMainland China
4th rowUnited States
5th rowSpain
ValueCountFrequency (%)
united 542
25.8%
states 418
19.9%
kingdom 123
 
5.8%
germany 80
 
3.8%
india 79
 
3.8%
brazil 64
 
3.0%
canada 62
 
2.9%
france 55
 
2.6%
netherlands 53
 
2.5%
israel 50
 
2.4%
Other values (59) 578
27.5%
2024-01-14T11:01:48.251951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1578
11.2%
t 1560
11.1%
e 1540
11.0%
n 1369
9.7%
i 1168
 
8.3%
d 986
 
7.0%
706
 
5.0%
s 608
 
4.3%
S 584
 
4.2%
U 545
 
3.9%
Other values (36) 3407
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11241
80.0%
Uppercase Letter 2104
 
15.0%
Space Separator 706
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1578
14.0%
t 1560
13.9%
e 1540
13.7%
n 1369
12.2%
i 1168
10.4%
d 986
8.8%
s 608
 
5.4%
r 514
 
4.6%
l 370
 
3.3%
o 277
 
2.5%
Other values (14) 1271
11.3%
Uppercase Letter
ValueCountFrequency (%)
S 584
27.8%
U 545
25.9%
I 169
 
8.0%
K 150
 
7.1%
C 112
 
5.3%
G 91
 
4.3%
B 82
 
3.9%
N 71
 
3.4%
F 64
 
3.0%
A 55
 
2.6%
Other values (11) 181
 
8.6%
Space Separator
ValueCountFrequency (%)
706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13345
95.0%
Common 706
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1578
11.8%
t 1560
11.7%
e 1540
11.5%
n 1369
10.3%
i 1168
8.8%
d 986
 
7.4%
s 608
 
4.6%
S 584
 
4.4%
U 545
 
4.1%
r 514
 
3.9%
Other values (35) 2893
21.7%
Common
ValueCountFrequency (%)
706
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1578
11.2%
t 1560
11.1%
e 1540
11.0%
n 1369
9.7%
i 1168
 
8.3%
d 986
 
7.0%
706
 
5.0%
s 608
 
4.3%
S 584
 
4.2%
U 545
 
3.9%
Other values (36) 3407
24.2%

state
Text

MISSING 

Distinct123
Distinct (%)22.9%
Missing948
Missing (%)63.9%
Memory size11.7 KiB
2024-01-14T11:01:48.435702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length8.7817164
Min length1

Characters and Unicode

Total characters4707
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)14.2%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowOhio
5th rowNorth Carolina
ValueCountFrequency (%)
california 148
23.2%
new 56
 
8.8%
york 43
 
6.8%
colorado 21
 
3.3%
illinois 19
 
3.0%
oregon 17
 
2.7%
north 16
 
2.5%
carolina 16
 
2.5%
texas 15
 
2.4%
massachusetts 12
 
1.9%
Other values (129) 274
43.0%
2024-01-14T11:01:48.842984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 613
13.0%
i 524
11.1%
o 438
 
9.3%
r 387
 
8.2%
n 377
 
8.0%
l 297
 
6.3%
e 275
 
5.8%
C 215
 
4.6%
s 192
 
4.1%
f 159
 
3.4%
Other values (50) 1230
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3901
82.9%
Uppercase Letter 678
 
14.4%
Space Separator 107
 
2.3%
Dash Punctuation 18
 
0.4%
Other Punctuation 2
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 613
15.7%
i 524
13.4%
o 438
11.2%
r 387
9.9%
n 377
9.7%
l 297
7.6%
e 275
7.0%
s 192
 
4.9%
f 159
 
4.1%
t 121
 
3.1%
Other values (21) 518
13.3%
Uppercase Letter
ValueCountFrequency (%)
C 215
31.7%
N 93
13.7%
Y 46
 
6.8%
M 41
 
6.0%
O 36
 
5.3%
I 29
 
4.3%
W 26
 
3.8%
T 22
 
3.2%
A 19
 
2.8%
B 19
 
2.8%
Other values (15) 132
19.5%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4579
97.3%
Common 128
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 613
13.4%
i 524
11.4%
o 438
 
9.6%
r 387
 
8.5%
n 377
 
8.2%
l 297
 
6.5%
e 275
 
6.0%
C 215
 
4.7%
s 192
 
4.2%
f 159
 
3.5%
Other values (46) 1102
24.1%
Common
ValueCountFrequency (%)
107
83.6%
- 18
 
14.1%
, 2
 
1.6%
1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4697
99.8%
None 10
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 613
13.1%
i 524
11.2%
o 438
 
9.3%
r 387
 
8.2%
n 377
 
8.0%
l 297
 
6.3%
e 275
 
5.9%
C 215
 
4.6%
s 192
 
4.1%
f 159
 
3.4%
Other values (44) 1220
26.0%
None
ValueCountFrequency (%)
ô 3
30.0%
á 3
30.0%
ã 1
 
10.0%
é 1
 
10.0%
í 1
 
10.0%
ó 1
 
10.0%

city
Text

MISSING 

Distinct728
Distinct (%)54.9%
Missing158
Missing (%)10.6%
Memory size11.7 KiB
2024-01-14T11:01:49.096236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length8.6146305
Min length2

Characters and Unicode

Total characters11423
Distinct characters85
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique575 ?
Unique (%)43.4%

Sample

1st rowAmersfoort
2nd rowSan Francisco
3rd rowHong Kong SAR
4th rowSan Leandro
5th rowSan Sebastián
ValueCountFrequency (%)
san 55
 
3.2%
london 45
 
2.6%
new 39
 
2.3%
singapore 36
 
2.1%
francisco 28
 
1.6%
los 26
 
1.5%
york 26
 
1.5%
angeles 24
 
1.4%
mumbai 21
 
1.2%
berlin 18
 
1.1%
Other values (809) 1381
81.3%
2024-01-14T11:01:49.389360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1140
 
10.0%
e 985
 
8.6%
n 950
 
8.3%
o 915
 
8.0%
i 690
 
6.0%
r 688
 
6.0%
l 553
 
4.8%
s 442
 
3.9%
t 416
 
3.6%
410
 
3.6%
Other values (75) 4234
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9240
80.9%
Uppercase Letter 1721
 
15.1%
Space Separator 410
 
3.6%
Other Punctuation 23
 
0.2%
Dash Punctuation 21
 
0.2%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Modifier Letter 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1140
12.3%
e 985
10.7%
n 950
10.3%
o 915
9.9%
i 690
 
7.5%
r 688
 
7.4%
l 553
 
6.0%
s 442
 
4.8%
t 416
 
4.5%
u 338
 
3.7%
Other values (39) 2123
23.0%
Uppercase Letter
ValueCountFrequency (%)
S 241
14.0%
B 175
 
10.2%
L 145
 
8.4%
A 128
 
7.4%
M 124
 
7.2%
C 113
 
6.6%
P 100
 
5.8%
N 79
 
4.6%
H 71
 
4.1%
T 63
 
3.7%
Other values (17) 482
28.0%
Other Punctuation
ValueCountFrequency (%)
. 8
34.8%
, 8
34.8%
' 7
30.4%
Space Separator
ValueCountFrequency (%)
410
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10951
95.9%
Common 462
 
4.0%
Cyrillic 10
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1140
 
10.4%
e 985
 
9.0%
n 950
 
8.7%
o 915
 
8.4%
i 690
 
6.3%
r 688
 
6.3%
l 553
 
5.0%
s 442
 
4.0%
t 416
 
3.8%
u 338
 
3.1%
Other values (57) 3834
35.0%
Common
ValueCountFrequency (%)
410
88.7%
- 21
 
4.5%
. 8
 
1.7%
, 8
 
1.7%
' 7
 
1.5%
) 3
 
0.6%
( 3
 
0.6%
ʻ 1
 
0.2%
1
 
0.2%
Cyrillic
ValueCountFrequency (%)
в 2
20.0%
к 1
10.0%
с 1
10.0%
о 1
10.0%
М 1
10.0%
и 1
10.0%
К 1
10.0%
е 1
10.0%
а 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11352
99.4%
None 60
 
0.5%
Cyrillic 10
 
0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1140
 
10.0%
e 985
 
8.7%
n 950
 
8.4%
o 915
 
8.1%
i 690
 
6.1%
r 688
 
6.1%
l 553
 
4.9%
s 442
 
3.9%
t 416
 
3.7%
410
 
3.6%
Other values (49) 4163
36.7%
None
ValueCountFrequency (%)
ã 15
25.0%
ö 9
15.0%
ó 7
11.7%
ü 6
 
10.0%
é 5
 
8.3%
í 4
 
6.7%
â 2
 
3.3%
á 2
 
3.3%
ç 2
 
3.3%
ä 2
 
3.3%
Other values (6) 6
 
10.0%
Cyrillic
ValueCountFrequency (%)
в 2
20.0%
к 1
10.0%
с 1
10.0%
о 1
10.0%
М 1
10.0%
и 1
10.0%
К 1
10.0%
е 1
10.0%
а 1
10.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

website
Text

Distinct1474
Distinct (%)99.9%
Missing8
Missing (%)0.5%
Memory size11.7 KiB
2024-01-14T11:01:49.562784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length292
Median length90
Mean length27.995935
Min length9

Characters and Unicode

Total characters41322
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1472 ?
Unique (%)99.7%

Sample

1st rowhttps://www.frieslandcampina.com/
2nd rowhttps://finlessfoods.com/
3rd rowhttps://www.avantmeats.com/
4th rowhttp://www.memphismeats.com/
5th rowhttps://biotech-foods.com/
ValueCountFrequency (%)
3
 
0.2%
https://www.hersheyland.in/sofit 2
 
0.1%
foods 2
 
0.1%
https://fresh-start.co.il/portfolio 2
 
0.1%
https://www.healthyproteins.nl 1
 
0.1%
https://steakholderfoods.com 1
 
0.1%
http://www.memphismeats.com 1
 
0.1%
https://biotech-foods.com 1
 
0.1%
https://www.aleph-farms.com 1
 
0.1%
https://scififoods.com 1
 
0.1%
Other values (1480) 1480
99.0%
2024-01-14T11:01:49.892988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4294
 
10.4%
t 3868
 
9.4%
o 3153
 
7.6%
w 2719
 
6.6%
. 2543
 
6.2%
s 2433
 
5.9%
e 2115
 
5.1%
p 1854
 
4.5%
h 1850
 
4.5%
c 1820
 
4.4%
Other values (67) 14673
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32116
77.7%
Other Punctuation 8272
 
20.0%
Uppercase Letter 341
 
0.8%
Dash Punctuation 272
 
0.7%
Decimal Number 230
 
0.6%
Space Separator 35
 
0.1%
Math Symbol 33
 
0.1%
Connector Punctuation 21
 
0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3868
12.0%
o 3153
 
9.8%
w 2719
 
8.5%
s 2433
 
7.6%
e 2115
 
6.6%
p 1854
 
5.8%
h 1850
 
5.8%
c 1820
 
5.7%
m 1687
 
5.3%
a 1683
 
5.2%
Other values (16) 8934
27.8%
Uppercase Letter
ValueCountFrequency (%)
A 25
 
7.3%
C 21
 
6.2%
F 21
 
6.2%
T 18
 
5.3%
S 17
 
5.0%
E 17
 
5.0%
N 16
 
4.7%
M 16
 
4.7%
Y 14
 
4.1%
R 13
 
3.8%
Other values (16) 163
47.8%
Decimal Number
ValueCountFrequency (%)
2 43
18.7%
0 32
13.9%
1 25
10.9%
3 25
10.9%
4 23
10.0%
8 23
10.0%
5 19
8.3%
6 18
7.8%
9 14
 
6.1%
7 8
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/ 4294
51.9%
. 2543
30.7%
: 1380
 
16.7%
? 23
 
0.3%
% 14
 
0.2%
& 8
 
0.1%
# 7
 
0.1%
, 2
 
< 0.1%
! 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 29
87.9%
+ 4
 
12.1%
Dash Punctuation
ValueCountFrequency (%)
- 272
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 21
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32457
78.5%
Common 8865
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3868
11.9%
o 3153
 
9.7%
w 2719
 
8.4%
s 2433
 
7.5%
e 2115
 
6.5%
p 1854
 
5.7%
h 1850
 
5.7%
c 1820
 
5.6%
m 1687
 
5.2%
a 1683
 
5.2%
Other values (42) 9275
28.6%
Common
ValueCountFrequency (%)
/ 4294
48.4%
. 2543
28.7%
: 1380
 
15.6%
- 272
 
3.1%
2 43
 
0.5%
35
 
0.4%
0 32
 
0.4%
= 29
 
0.3%
1 25
 
0.3%
3 25
 
0.3%
Other values (15) 187
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4294
 
10.4%
t 3868
 
9.4%
o 3153
 
7.6%
w 2719
 
6.6%
. 2543
 
6.2%
s 2433
 
5.9%
e 2115
 
5.1%
p 1854
 
4.5%
h 1850
 
4.5%
c 1820
 
4.4%
Other values (67) 14673
35.5%

year_founded
Real number (ℝ)

MISSING 

Distinct112
Distinct (%)7.6%
Missing19
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2007.9399
Minimum1668
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-01-14T11:01:50.051060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1668
5-th percentile1959.2
Q12010
median2017
Q32020
95-th percentile2022
Maximum2023
Range355
Interquartile range (IQR)10

Descriptive statistics

Standard deviation26.797315
Coefficient of variation (CV)0.013345676
Kurtosis34.664997
Mean2007.9399
Median Absolute Deviation (MAD)4
Skewness-4.7135086
Sum2941632
Variance718.09612
MonotonicityNot monotonic
2024-01-14T11:01:50.175137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021 173
 
11.7%
2020 170
 
11.5%
2019 158
 
10.6%
2018 110
 
7.4%
2022 95
 
6.4%
2017 93
 
6.3%
2016 72
 
4.9%
2015 64
 
4.3%
2014 41
 
2.8%
2013 36
 
2.4%
Other values (102) 453
30.5%
ValueCountFrequency (%)
1668 1
0.1%
1752 1
0.1%
1765 1
0.1%
1834 1
0.1%
1854 1
0.1%
1857 1
0.1%
1860 1
0.1%
1862 1
0.1%
1865 1
0.1%
1866 1
0.1%
ValueCountFrequency (%)
2023 21
 
1.4%
2022 95
6.4%
2021 173
11.7%
2020 170
11.5%
2019 158
10.6%
2018 110
7.4%
2017 93
6.3%
2016 72
4.9%
2015 64
 
4.3%
2014 41
 
2.8%

founders
Text

MISSING 

Distinct844
Distinct (%)99.3%
Missing634
Missing (%)42.7%
Memory size11.7 KiB
2024-01-14T11:01:50.461937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length143
Median length70
Mean length25.082353
Min length3

Characters and Unicode

Total characters21320
Distinct characters89
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique838 ?
Unique (%)98.6%

Sample

1st rowMike Selden, Brian Wyrwas
2nd rowCarrie Chan, Mario Chin
3rd rowUma Valeti, Nicholas Genovese, Will Clem
4th rowMercedes Vila Juarez
5th rowDidier Toubia, Prof. Shulamit Levenberg
ValueCountFrequency (%)
and 62
 
2.0%
dr 42
 
1.3%
27
 
0.8%
david 24
 
0.8%
michael 16
 
0.5%
van 12
 
0.4%
john 12
 
0.4%
de 12
 
0.4%
ben 10
 
0.3%
phd 9
 
0.3%
Other values (2345) 2953
92.9%
2024-01-14T11:01:50.841901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2407
 
11.3%
a 2059
 
9.7%
e 1641
 
7.7%
n 1484
 
7.0%
i 1365
 
6.4%
r 1219
 
5.7%
o 981
 
4.6%
l 825
 
3.9%
s 682
 
3.2%
t 624
 
2.9%
Other values (79) 8033
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14963
70.2%
Uppercase Letter 3266
 
15.3%
Space Separator 2407
 
11.3%
Other Punctuation 624
 
2.9%
Dash Punctuation 39
 
0.2%
Close Punctuation 6
 
< 0.1%
Control 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Final Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2059
13.8%
e 1641
11.0%
n 1484
9.9%
i 1365
 
9.1%
r 1219
 
8.1%
o 981
 
6.6%
l 825
 
5.5%
s 682
 
4.6%
t 624
 
4.2%
h 620
 
4.1%
Other values (36) 3463
23.1%
Uppercase Letter
ValueCountFrequency (%)
S 291
 
8.9%
M 267
 
8.2%
A 245
 
7.5%
D 212
 
6.5%
B 202
 
6.2%
C 191
 
5.8%
R 174
 
5.3%
J 171
 
5.2%
L 166
 
5.1%
G 149
 
4.6%
Other values (19) 1198
36.7%
Other Punctuation
ValueCountFrequency (%)
, 514
82.4%
. 72
 
11.5%
& 19
 
3.0%
; 9
 
1.4%
" 4
 
0.6%
' 3
 
0.5%
/ 3
 
0.5%
Space Separator
ValueCountFrequency (%)
2407
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18229
85.5%
Common 3091
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2059
 
11.3%
e 1641
 
9.0%
n 1484
 
8.1%
i 1365
 
7.5%
r 1219
 
6.7%
o 981
 
5.4%
l 825
 
4.5%
s 682
 
3.7%
t 624
 
3.4%
h 620
 
3.4%
Other values (65) 6729
36.9%
Common
ValueCountFrequency (%)
2407
77.9%
, 514
 
16.6%
. 72
 
2.3%
- 39
 
1.3%
& 19
 
0.6%
; 9
 
0.3%
) 6
 
0.2%
6
 
0.2%
( 6
 
0.2%
" 4
 
0.1%
Other values (4) 9
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21248
99.7%
None 70
 
0.3%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2407
 
11.3%
a 2059
 
9.7%
e 1641
 
7.7%
n 1484
 
7.0%
i 1365
 
6.4%
r 1219
 
5.7%
o 981
 
4.6%
l 825
 
3.9%
s 682
 
3.2%
t 624
 
2.9%
Other values (55) 7961
37.5%
None
ValueCountFrequency (%)
é 10
14.3%
í 9
12.9%
á 8
11.4%
ä 6
 
8.6%
ó 6
 
8.6%
ö 6
 
8.6%
ü 3
 
4.3%
ë 2
 
2.9%
å 2
 
2.9%
Á 2
 
2.9%
Other values (13) 16
22.9%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

logo
Text

MISSING 

Distinct1362
Distinct (%)100.0%
Missing122
Missing (%)8.2%
Memory size11.7 KiB
2024-01-14T11:01:51.007560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length1391
Median length440
Mean length314.87812
Min length268

Characters and Unicode

Total characters428864
Distinct characters96
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1362 ?
Unique (%)100.0%

Sample

1st rowlogo-fc-full.svg (https://v5.airtableusercontent.com/v2/24/24/1704348000000/Tg3uMBRitigAguBL0qF3_A/iR5vowCsEncGcAhcxNrWVszk4G1LO5nx0uhyKps--8LseWCX-o817MPf_4c8f3FJlYRPstiZlibULyimpFU1UbsYt1XYpV-EqTHcjiSvWzBZjHSBSVVf5Nq3UNzyODsjU8Ro2JRVRs3fdfHI3IWjQQ/j2zvwRTylMGhxfEObsnm_Japb4qeY3pp6zlfbEaeheE)
2nd rowCapture.JPG (https://v5.airtableusercontent.com/v2/24/24/1704348000000/nLNZFIU-kl24aajV9GLVvQ/LP9jHcdV9VxzZ87FECoDy6VhVaqC4LnARFc9rw00vNA468S8g14PbTm_752DBdegSrpKgAx33gx2GmKujlay9uqZr2Z6ns4xoyFVw7RY7mhu9NYTUnrzBhEKBqbWnu-lu0otyqSEciz_hNQoGcLYww/hVC5thz8Z_NmHLnvUMEO_6dnatA30yShUoITJa9jdJ0)
3rd rowtbnkq9whzxvh5fxpj6uj.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/9FR4VKSongtb4HvDJ12g6w/kDkqWhXpFZtVqoH2DrztU0n9g0WpE0TY5kCxbULM5JhdW9Lw0xSBikeeuhObogABFP9c5NlAks2JF55wFxep-WXRRPupqyuRXfi_X9Z6o4q2S1cwYj-9H1tG9X5YFJmYaNKTz2hEjOK3Uh9R4JxriGPpW7m2hUxo-sZvl5208xU/nhc4HxWyscty8Qpp1Cm9rSPvMcQ7ntPJ_ysD8mdI2tQ)
4th rowHorizontal_Black.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/BKaaWQiCH_Lpnu6m8Vrkkg/hh8rect9mXLS7bAXc1Re_-5lY4X1c-msfW4mRW25K1J3ZAKtQmfacWAx3RmC8Y74FRkCGSQ1wxj59EqwfDXCyobl8hIUMC9w0icXqJnpGWD6GU9Atuz4-AV1W4DZ_Cgkk_tNTNCjBOAdBGmYBXq6lA/Tfdob934fAtv_1U5OvyVS3Me3ByYudMgbwh5YSf4iiY)
5th rowdko8ln1xykb67qgiek0w.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/k1v_-sAi_o21OczvLh21bQ/Ru57AFfB_XDjCDZeM6KFNk1734hqPc6xdJdsSk3oxMQxQeup1T8rsQQQEaTsYQxA39QCa6EUXXmnCiFsrolkuSbxVSneeI2jMdAkxCBVcGX2yuzanHfR3BVOhIvTvPOwrJsB1-51xzxFQw2SRmkoGufzyd7Hc0ZA9Rgp8I_6Z94/P4yZdnFMQCR5FPDPitZKxNp8OjAdFtnIv5UC53djSUw)
ValueCountFrequency (%)
screenshot 166
 
4.2%
pm.png 131
 
3.3%
at 106
 
2.7%
am.png 67
 
1.7%
logo 67
 
1.7%
screen 63
 
1.6%
shot 63
 
1.6%
capture.jpg 49
 
1.2%
logo.png 35
 
0.9%
download 21
 
0.5%
Other values (2902) 3181
80.6%
2024-01-14T11:01:51.291331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15672
 
3.7%
t 12465
 
2.9%
/ 12330
 
2.9%
4 10951
 
2.6%
e 10413
 
2.4%
2 10095
 
2.4%
o 9917
 
2.3%
n 9092
 
2.1%
s 8550
 
2.0%
a 8381
 
2.0%
Other values (86) 320998
74.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183506
42.8%
Uppercase Letter 129457
30.2%
Decimal Number 80853
18.9%
Other Punctuation 18142
 
4.2%
Dash Punctuation 5899
 
1.4%
Connector Punctuation 5489
 
1.3%
Space Separator 2588
 
0.6%
Close Punctuation 1439
 
0.3%
Open Punctuation 1439
 
0.3%
Math Symbol 34
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12465
 
6.8%
e 10413
 
5.7%
o 9917
 
5.4%
n 9092
 
5.0%
s 8550
 
4.7%
a 8381
 
4.6%
r 8353
 
4.6%
c 8231
 
4.5%
v 7811
 
4.3%
p 7679
 
4.2%
Other values (17) 92614
50.5%
Uppercase Letter
ValueCountFrequency (%)
A 5681
 
4.4%
Q 5480
 
4.2%
M 5405
 
4.2%
S 5163
 
4.0%
G 5108
 
3.9%
P 5087
 
3.9%
O 5023
 
3.9%
B 4992
 
3.9%
Y 4982
 
3.8%
I 4966
 
3.8%
Other values (16) 77570
59.9%
Decimal Number
ValueCountFrequency (%)
0 15672
19.4%
4 10951
13.5%
2 10095
12.5%
1 7172
8.9%
5 6665
8.2%
3 6665
8.2%
7 6538
8.1%
8 6489
8.0%
9 5308
 
6.6%
6 5298
 
6.6%
Other Punctuation
ValueCountFrequency (%)
/ 12330
68.0%
. 4409
 
24.3%
: 1370
 
7.6%
@ 15
 
0.1%
, 9
 
< 0.1%
& 4
 
< 0.1%
" 2
 
< 0.1%
! 1
 
< 0.1%
' 1
 
< 0.1%
% 1
 
< 0.1%
Other Letter
ValueCountFrequency (%)
로 3
20.0%
고 3
20.0%
플 2
13.3%
닛 1
 
6.7%
상 1
 
6.7%
심 1
 
6.7%
도 1
 
6.7%
해 1
 
6.7%
래 1
 
6.7%
가 1
 
6.7%
Space Separator
ValueCountFrequency (%)
2584
99.8%
  4
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 1438
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1438
99.9%
[ 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 33
97.1%
= 1
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 5899
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5489
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̀ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 1
100.0%
Other Symbol
ValueCountFrequency (%)
┬ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 312963
73.0%
Common 115885
 
27.0%
Hangul 15
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12465
 
4.0%
e 10413
 
3.3%
o 9917
 
3.2%
n 9092
 
2.9%
s 8550
 
2.7%
a 8381
 
2.7%
r 8353
 
2.7%
c 8231
 
2.6%
v 7811
 
2.5%
p 7679
 
2.5%
Other values (43) 222071
71.0%
Common
ValueCountFrequency (%)
0 15672
13.5%
/ 12330
10.6%
4 10951
 
9.4%
2 10095
 
8.7%
1 7172
 
6.2%
5 6665
 
5.8%
3 6665
 
5.8%
7 6538
 
5.6%
8 6489
 
5.6%
- 5899
 
5.1%
Other values (22) 27409
23.7%
Hangul
ValueCountFrequency (%)
로 3
20.0%
고 3
20.0%
플 2
13.3%
닛 1
 
6.7%
상 1
 
6.7%
심 1
 
6.7%
도 1
 
6.7%
해 1
 
6.7%
래 1
 
6.7%
가 1
 
6.7%
Inherited
ValueCountFrequency (%)
̀ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428841
> 99.9%
Hangul 15
 
< 0.1%
Punctuation 4
 
< 0.1%
None 2
 
< 0.1%
Diacriticals 1
 
< 0.1%
Box Drawing 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15672
 
3.7%
t 12465
 
2.9%
/ 12330
 
2.9%
4 10951
 
2.6%
e 10413
 
2.4%
2 10095
 
2.4%
o 9917
 
2.3%
n 9092
 
2.1%
s 8550
 
2.0%
a 8381
 
2.0%
Other values (71) 320975
74.8%
Punctuation
ValueCountFrequency (%)
  4
100.0%
Hangul
ValueCountFrequency (%)
로 3
20.0%
고 3
20.0%
플 2
13.3%
닛 1
 
6.7%
상 1
 
6.7%
심 1
 
6.7%
도 1
 
6.7%
해 1
 
6.7%
래 1
 
6.7%
가 1
 
6.7%
None
ValueCountFrequency (%)
í 1
50.0%
« 1
50.0%
Diacriticals
ValueCountFrequency (%)
̀ 1
100.0%
Box Drawing
ValueCountFrequency (%)
┬ 1
100.0%

Interactions

2024-01-14T11:01:43.409812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-14T11:01:43.564116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T11:01:43.769296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

companybrief_descriptionprotein_categorycompany_focuscompany_typetechnology_focusproduct_typeanimal_type_analogingredient_typeoperating_regionscountry_regionstatecitywebsiteyear_foundedfounderslogo
0FrieslandCampinaDutch multinational dairy cooperative that has launched plant-based milks. In 2023, launched plant-based chicken brand Tender'lish.Plant-basedDairyDiversifiedEnd product formulation and manufacturingMilkNaNOat,SoyEuropeNetherlandsNaNAmersfoorthttps://www.frieslandcampina.com/2008.0NaNlogo-fc-full.svg (https://v5.airtableusercontent.com/v2/24/24/1704348000000/Tg3uMBRitigAguBL0qF3_A/iR5vowCsEncGcAhcxNrWVszk4G1LO5nx0uhyKps--8LseWCX-o817MPf_4c8f3FJlYRPstiZlibULyimpFU1UbsYt1XYpV-EqTHcjiSvWzBZjHSBSVVf5Nq3UNzyODsjU8Ro2JRVRs3fdfHI3IWjQQ/j2zvwRTylMGhxfEObsnm_Japb4qeY3pp6zlfbEaeheE)
1Finless FoodsU.S.-based company working on plant-based fish and cultivated blue fin tunaCultivated,Plant-basedMeat,SeafoodSpecialized (focused on alternative proteins)Cell culture media,End product formulation and manufacturingWhole muscle meat/seafoodFishNaNU.S. and CanadaUnited StatesCaliforniaSan Franciscohttps://finlessfoods.com/2017.0Mike Selden, Brian WyrwasCapture.JPG (https://v5.airtableusercontent.com/v2/24/24/1704348000000/nLNZFIU-kl24aajV9GLVvQ/LP9jHcdV9VxzZ87FECoDy6VhVaqC4LnARFc9rw00vNA468S8g14PbTm_752DBdegSrpKgAx33gx2GmKujlay9uqZr2Z6ns4xoyFVw7RY7mhu9NYTUnrzBhEKBqbWnu-lu0otyqSEciz_hNQoGcLYww/hVC5thz8Z_NmHLnvUMEO_6dnatA30yShUoITJa9jdJ0)
2Avant MeatsHong Kong-based company using proprietary biotechnology platform to produce cultivated fish products, including food, skincare, and other functional applicationsCultivatedMeat,SeafoodSpecialized (focused on alternative proteins)End product formulation and manufacturingGround meat/seafoodFishNaNAsia PacificMainland ChinaNaNHong Kong SARhttps://www.avantmeats.com/2018.0Carrie Chan, Mario Chintbnkq9whzxvh5fxpj6uj.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/9FR4VKSongtb4HvDJ12g6w/kDkqWhXpFZtVqoH2DrztU0n9g0WpE0TY5kCxbULM5JhdW9Lw0xSBikeeuhObogABFP9c5NlAks2JF55wFxep-WXRRPupqyuRXfi_X9Z6o4q2S1cwYj-9H1tG9X5YFJmYaNKTz2hEjOK3Uh9R4JxriGPpW7m2hUxo-sZvl5208xU/nhc4HxWyscty8Qpp1Cm9rSPvMcQ7ntPJ_ysD8mdI2tQ)
3Upside FoodsU.S.-based cultivated meat startup producing beef, duck, and chicken product prototypesCultivatedMeatSpecialized (focused on alternative proteins)Cell line development,End product formulation and manufacturingNaNDuck,Beef/veal,ChickenNaNU.S. and CanadaUnited StatesCaliforniaSan Leandrohttp://www.memphismeats.com/2015.0Uma Valeti, Nicholas Genovese, Will ClemHorizontal_Black.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/BKaaWQiCH_Lpnu6m8Vrkkg/hh8rect9mXLS7bAXc1Re_-5lY4X1c-msfW4mRW25K1J3ZAKtQmfacWAx3RmC8Y74FRkCGSQ1wxj59EqwfDXCyobl8hIUMC9w0icXqJnpGWD6GU9Atuz4-AV1W4DZ_Cgkk_tNTNCjBOAdBGmYBXq6lA/Tfdob934fAtv_1U5OvyVS3Me3ByYudMgbwh5YSf4iiY)
4BioTech FoodsSpain-based startup producing cultivated meat products, acquired by Brazil-based JBS in 2021CultivatedMeatSpecialized (focused on alternative proteins)End product formulation and manufacturingGround meat/seafoodBeef/vealNaNEuropeSpainNaNSan Sebastiánhttps://biotech-foods.com/2017.0Mercedes Vila Juarezdko8ln1xykb67qgiek0w.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/k1v_-sAi_o21OczvLh21bQ/Ru57AFfB_XDjCDZeM6KFNk1734hqPc6xdJdsSk3oxMQxQeup1T8rsQQQEaTsYQxA39QCa6EUXXmnCiFsrolkuSbxVSneeI2jMdAkxCBVcGX2yuzanHfR3BVOhIvTvPOwrJsB1-51xzxFQw2SRmkoGufzyd7Hc0ZA9Rgp8I_6Z94/P4yZdnFMQCR5FPDPitZKxNp8OjAdFtnIv5UC53djSUw)
5Aleph FarmsIsrael-based company producing cultivated beef steakCultivatedMeatSpecialized (focused on alternative proteins)End product formulation and manufacturing,Scaffolding and structureGround meat/seafood,Whole muscle meat/seafoodBeef/vealNaNAfrica/Middle EastIsraelNaNRehovothttps://www.aleph-farms.com/2017.0Didier Toubia, Prof. Shulamit Levenbergfwhce84ehyiy1hxs6qro.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/HBbFPoCAU3Ig5tHlAYMLJw/V0Me3eQqvj8bxjh8I1uN07_lP1avZfi5KUdJhT9RQ3IBLqfmDfIIPkQVTCfVcv86nryYueQyeQZl9nVGRAMACPrlnpd1GjwdtInHGaLn0DXiWCyq1Jd768lhFLiXNK-PPcB4zYzhSA_1465CVl8mMRDCv-ZUTE9t2RGdHHf2jKM/F46W3mhcmQFNok7i-tK0D1a_JgdCZltMnedXlAYyu60)
6SCiFi FoodsU.S.-based start-up working on cultivated and cultured meat products (formerly Artemys Foods)CultivatedMeatSpecialized (focused on alternative proteins)End product formulation and manufacturingGround meat/seafoodBeef/vealNaNU.S. and CanadaUnited StatesCaliforniaSan Franciscohttps://scififoods.com/2019.0Jessica Krieger, Joshua Marchncvgvj1raxbf6q4zpkgv.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/Szb8_C-qZWJWIN_ERJBIcw/ivy-8mAvxwSGFX2qNx8ukBzsV9rAgbb34nNd967U7YbqqO3LKiTeBIBVXk2HkSIOcXw9p8edJWYQSC3YFPwe4BsmmdEA7BAZFu2HTutgZIFZbYtgT92NRTpmpod-_mMBUAtLBK1SSXOGpOnvZ-oHLrCmWJvWac2a8rQRY6RNLYE/j9PBoCNTYTi0uiTOJc4XNpB4-WSb_c2B__TauR_FC4k)
7Matrix F.T.Startup making 3D nanofiber tubes for cultivated meat production.CultivatedMeatSpecialized (focused on alternative proteins)Scaffolding and structureNaNNaNNaNU.S. and CanadaUnited StatesOhioDublinhttps://www.matrixmeats.com/2019.0Flavio LobatoMATRIXFT_Logo.jpeg (https://v5.airtableusercontent.com/v2/24/24/1704348000000/htzthSQyQXx4RnkWirDjqA/lDv8f5BNkpjqKfx5uZRANHO-Wp-IIohUM_lwrZFgJyye0AGt44a9OVFp5IpB406ABI26CX8VPvxpdv6j2qMG6CYEtF72xfHnXaX443wBtXOnWtQRIKp8TtFMAWeQ92VuMlHe10ufaQmXDyjGPH30mQ/Zy0BC0eae696UaUhXraVrsONB8vJ1WgTN5kYCkfOFEg)
8Shiok MeatsSingapore-based company producing cultivated seafood, including crustaceans like shrimps, crabs, and lobstersCultivatedSeafoodSpecialized (focused on alternative proteins)End product formulation and manufacturingWhole muscle meat/seafoodFish,Shellfish,CrabNaNAsia PacificSingaporeNaNSingaporehttps://shiokmeats.com/2018.0Sandhya Sriram, Ka Yi LingScreen Shot 2021-02-08 at 2_19_19 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/pAlh2Ebp6nq_FHLL_3ruNw/vu4T5rBhjX1a33eZ4JUbHlIlTOxtjGrj4IphWsoJodIZAuIE0OpLAO4uxAsepmuQD1ltD7qb1vHlOas9HnXZ3XMn9qL5s2VXDDKKa7_-CqaLby87az1MxK22AAE6fvMNcGGI3kG-2YMcpXf4ZSRf_5p7PcJTo_d3CgcRea0S1d8/ddn-JVUuzXgMBslzmm439cmOb5szsCRZgigdkJU1aNg)
9Newform FoodsFormerly known as Mzansi Meat Co., Newform Foods' mission is to reinvent food systems for the benefit of our planet, our health and the lives of animals.CultivatedMeatSpecialized (focused on alternative proteins)Cell culture media,End product formulation and manufacturingWhole muscle meat/seafoodBeef/veal,ChickenNaNAfrica/Middle EastSouth AfricaNaNCape Townhttps://newformfoods.co.uk/2020.0Jay Van Der Walt, Brett ThompsonScreenshot 2023-08-25 at 2.54.40 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/VKBRkqgM6zZhw-SYrrrY9A/oh-nSYmJvq1CgPNiRKA8tOmPSd7bg61W98PxfMgQUA-Sq0FGQdWTDcJVD4Etg7OSpYPlRkVydblKwE7XX0AQW2BtrkzbK_Tm_sw61FNqxYbAFM9ydby9d2OAra9iVMpfWoiRt4Rq-XLZU3EipxEZRwemO-243uc4oWhxeUndTcc/LZLEYOI30P5bdXi9IBba0ENTfivgjhuqj39fxA8PQ6o)
companybrief_descriptionprotein_categorycompany_focuscompany_typetechnology_focusproduct_typeanimal_type_analogingredient_typeoperating_regionscountry_regionstatecitywebsiteyear_foundedfounderslogo
1474Renaissance BioScience Corp.Biotechnology company specializing in yeast, Saccharomyces cerevisiae, and precision fermentation to develop clean technologies/ingredients for the food and beverage industry. We collaborate with alternative protein companies utilizing plant-based flours and proteins. We apply our expertise in mitigating undesirable flavors, aromas, and colors associated with these materials in plant-based applications. \nPrecision fermentation,Biomass fermentation,Traditional fermentationIngredients and inputs,OtherDiversifiedIngredient optimization,Bioprocess design,End product formulation and manufacturing,Host strain developmentNaNNaNYeastU.S. and CanadaCanadaNaNVancouverhttps://renaissancebioscience.com/2013.0Dr. John Husnik and Mr. Maurice BoucherRBSC.jpg (https://v5.airtableusercontent.com/v2/24/24/1704348000000/Itm8pkKIIy8-EvU3UnhdRQ/MDmn-fatTiBPDqawO7krwAEXiIU6VVFS3h0Px-_me8N31WW4ZtCcbD2f8WNqa-Msb6wqbT3sgLA24ElE_rAEVTmvQrlUyG2Oxl4ovl9XqvQY-KWuJxEEEOwFwPJn6_o-KR0zwwxiglRmw7wpqu4ezw/lN52g1rcC3Htdrw8p6woYvBDHoLtGGddVFOjSXzGVqM)
1475Arta BioanalyticsBioinformatics and data analysis support for cellular agricultureCultivated,Fermentation-derived,Traditional fermentation,Biomass fermentation,Precision fermentationMeat,Eggs,Dairy,Seafood,OtherSpecialized (focused on alternative proteins)Bioprocess design,Cell culture media,Ingredient optimization,Cell line developmentOtherNaNNaNAustralia/New ZealandAustraliaNaNSydneyhttps://www.artabioanalytics.com/2023.0Alex WardColor logo - no background.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/BbepD_g-ox0DkKG_Tgez2A/N0Znc3_nG_US_CbIYrSbrnJ1D5BuOWbT9UorSJAOYDL9df2v60GcCpT-NbbcE8Z4bOjuFr4pqstgcLOh20tmtHfKEACXNN_caWpK9pL7cqgzBVUdQSXIqNrHGn0I5xEmSTpGgnkMSKmp43W9hzs_dYOq6lKyzpDE_thnOlCsKrU/ViHXfmUKRVYseojOJg1JMD10-PoV2CKaSWApXg8smks)
1476MaizlyMaïzly specializes in the development and commercialization of corn-based milk-alternatives, custard, and infant formula.Plant-basedDairyDiversifiedNaNMilk,OtherNaNCornNaNNaNNaNNaNhttps://www.maizly.com/2019.0Tim Leclerq, Marcel Van Der MerweScreenshot 2023-12-14 at 2.50.55 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/Pwl38JWSr8pCVj7kKcb3Kw/ORJCSWuO1cAnHJeLvczW8cahZ2aqZ1NjVer6oK7zO9i23-LjIDqWSJYGSN_1SUdvAGYU8H68lH_5toKdbIXxWEDi2UuF2eQuOaYwN1MFbrnZUpg-d1TTC-KYIWmwlUK4CK2F2k9GT2AACfqmuHtnIVWYGW-As0z0J8OHRq2t_g4/zjVeNSUpcyfkGdVPpJzseKSPNt6Iod4OqjYOlp6Dncs)
1477LypidUS-based company that produces plant-based meat, such as pork belly and meatballs.Plant-basedMeat,Ingredients and inputsSpecialized (focused on alternative proteins)End product formulation and manufacturing,Ingredient optimizationWhole muscle meat/seafood,Oils and fatsPork,OtherNaNU.S. and CanadaUnited StatesCaliforniaSan Franciscowww.lypid.co2021.0Dr. Michelle Lee and Dr. Jen-Yu HuangLypid_Or-logo-03 (1).png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/C9ZEVEOkGUpbhgxvceg3AQ/eT2IlSz6RoBfONg1rN3HSFPILMiGDo2bGdUhvurWe5rxETZc-y1K-qxXKq7gAnvngGBjZlLxQ-JH0uNaPYgwg-a4UfQ4UmKHjpHOKbzhP5_yag84I8t-2Ol0v448hlhngzDrZWJMeV3uhM9pT6_P3t5YPy52aQovcK9shFF1w40/NzR6zJxCpk5lUBDAfJ-6yrMcmohriMTn7Hlfmy7Yph8)
1478Jata EmonaTraditonally animal food (eggs, meat) and feed production company, diversfying into plant based semi-finished and finished products. Moisture extrusion -- extruded and cut, pulled, sliced and minced products; moulded products -- burgers, nuggets, sea sticks, minced mass and marinated pulled products.Plant-basedMeat,Eggs,Seafood,Contract manufacturing/processingDiversifiedEnd product formulation and manufacturingWhole muscle meat/seafood,Other meat/seafood,Eggs,OtherBeef/veal,Chicken,FishSoy,PeaEuropeSloveniaNaNLjubljanawww.jata-emona.si1955.0NaNjata-emona-logo.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/uzlYxTAaeT3C8jcGnG72bg/BjkYJh6UatNyOP8mfOV7PWvdYsIGuxa3lQgTOw6DoGBp46UA6yhaMEM7Z4_HsgfR7DWDRhiXyG9vnMNH4EoCBG-BV2zIFXxEY-LnicJsUxjFY5SL9re9whViUOt1smRd5tmMFLEBv4FUzsp0TrlvFg/6EgW-rlBTi3S8vP1uP-4IOZuQUe1bCHizZmUS5LcVvs)
1479DELIGENE LTDDeligene uses plant genome editing to develop new seeds and create plant proteins and oils. \nPlant-basedIngredients and inputs,Meat,DairyDiversifiedCrop developmentNaNNaNSoyNaNNaNNaNNaNhttps://www.deligene.com/NaNPini Kamari, Erez Gal-Oz, Itay CohenNaN
1480WOOP4Plant-based seafood including salmon, tuna, piranha, and mahi-mahi made without major food allergens.Plant-basedSeafoodSpecialized (focused on alternative proteins)NaNWhole muscle meat/seafoodFish,Tuna,SalmonKonjacU.S. and CanadaCanadaQuebecBouchervillehttps://woop4.com/2023.0NaNScreenshot 2024-01-01 at 12.58.03 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/s_MMNjtcQjY0i13PxxkRVQ/91bx1mq1TZcvn50a3toGA_8-w_3RXqgvnGMXe6LR7bo0x2esiDxN30C9vP520YL6YD3agYelOD2WTycuhWvbF13TMjKQ91Tr1jto_VcJrZa-tZWQPgIxUKWOgU0iuQtbzAEyZ-lF68mpFJoziRoGnHS5kgUpBDS4kESxhqtMXZI/c2wy0SdbCKRzzxJo8bLu1pI66JAXYmEpbvWYo8bvXTU)
1481WonderMeatBrand of shelf-stable powdered plant-based meat made from soy and pea protein. GoodMorning Global parent company.Plant-basedMeatDiversifiedNaNOther meat/seafoodNaNPea,SoyAsia PacificMalaysiaNaNNaNNaNNaNNaNNaN
1482EFISHient ProteinCultivated-meat startup focused on seafood starting with tilapia. Collaboration between BioMeat FoodTech and the Volcani Institute.CultivatedSeafoodSpecialized (focused on alternative proteins)NaNWhole muscle meat/seafoodFishNaNAfrica/Middle EastIsraelNaNPetah Tikvahttps://efishient.co.il/2021.0NaNScreenshot 2024-01-03 at 1.28.43 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/d_peBkGUgU4PNKnbdNUVAw/XVD5Si5J2zYchmf-4POitJ4mwPfT2FHjIR3yzczPJHgfpzwi7jbNEbgpG9yGpMK7Xvz9dfYuwO24ZXrQMrDmSFMrIBJIUy-Cr8yqjQGKvAq19NLAHO_nCeZ-F8VmgysWszNwKP-tSvORhmoYstHpTzZtWxL8yX90uRwuKF31jOo/ULOAsEHOqzeqZfJW8UIEOG0_3oBssJkd1OnOYQl0WZQ)
1483Ocean KissPlant-based seafood company. SOLMON is their first product, smoked salmon.NaNSeafoodSpecialized (focused on alternative proteins)NaNWhole muscle meat/seafoodSalmonPea,Algae,MicroalgaeEuropeFranceNaNNaNhttps://www.ocean-kiss.com/NaNNaNScreenshot 2024-01-03 at 2.32.39 PM.png (https://v5.airtableusercontent.com/v2/24/24/1704348000000/dWmbUSYO7jPbwWUDviJ9rw/YjDrEfr3hguZYWzJDkowwCaOQTrKLufZI-hZuoNTQ95gH_qATDMzMM9Tq4yN6BZdoshLtrzT7HLHWRgkYGfplMGeEZNIUIaWhNIJiCgVLh3ydIcQXGAp_ihE-CnNKCuDw3vMWmE2RFf0-xSav1i9jPBnZ4Hjhan3h-lC3Dagegs/7j0NfDCOxSJalZoBY1RjrVYOqI-xaUgV5uLmQUYaSbI)